Introduction to R and Basic Data Analysis

ACTEX Learning - AFDP: R Technical Skill Project

Introduction

In this project assignment, you will be working on a dataset from the Casualty Actuarial Society (CAS) website. The dataset contains information on the workers’ compensation insurance industry in the United States. The dataset includes data on premiums, losses, and expenses for various years.

Objective

The goal of this assignment is to analyze and model workers’ compensation claims data using the wkcomp_pos dataset. You will explore patterns in incurred and paid losses, understand how they develop over time, and build a predictive model for cumulative paid losses using regression techniques.

Deliverables

  1. Report detailing:
  • Your data exploration process and key insights.
  • The regression model and its interpretation.
  • Model evaluation and assumptions check.
  • Optional: comparison between the linear model and the advanced model (e.g., GAM).
  1. R Code used for the analysis, clearly documented and structured.

Learning Outcomes

  • Gain experience with real-world workers’ compensation data.
  • Develop proficiency in data manipulation and visualization using tidyverse.
  • Build and evaluate regression models for loss prediction.
  • Understand the importance of model assumptions and how to check them visually.

Tasks

The project assignment is divided into four main tasks:

  1. Data Exploration: Load the dataset, check for missing values, and visualize key relationships.
  2. Modeling the Loss Development: Create a regression model to predict cumulative paid losses.
  3. Model Evaluation: Evaluate the model using metrics such as R-squared, RMSE, and AIC.
  4. Advanced Modeling (Optional): Try an advanced model (e.g., GAM) and compare its performance to the linear model.

References

Happy learning! Good luck!